{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "700c5360",
   "metadata": {},
   "source": [
    "## Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45dbd9c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fitting(A,b):\n",
    "    return np.linalg.inv(A.T@A)@(A.T@b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b95fd8ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calA(X):\n",
    "    #X=np.array(X)\n",
    "    A=np.zeros((X.size,2))\n",
    "    A[:,0]=1\n",
    "    A[:,1]=X\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7beccd1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calb(Y):\n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "716b9dda",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bfb87f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83dfa79f",
   "metadata": {},
   "outputs": [],
   "source": [
    "t=np.linspace(0,10,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2545f62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6eddc97",
   "metadata": {},
   "outputs": [],
   "source": [
    "R=1\n",
    "C=1\n",
    "i=np.array(5/R*np.exp(-t/(R*C))+(np.random.rand(t.size)-0.5)*0.0001)\n",
    "#                                     0~1的数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2483ebaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.00000759e+00, 1.83939645e+00, 6.76713149e-01, 2.48960836e-01,\n",
       "       9.16040426e-02, 3.36451575e-02, 1.24383444e-02, 4.51345351e-03,\n",
       "       1.69966094e-03, 5.75152702e-04, 1.82307907e-04])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59e65597",
   "metadata": {},
   "outputs": [],
   "source": [
    "b=calb(np.log(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d4efec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "A=calA(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac0ec8a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "result=fitting(A,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da9b4ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.64523896, -1.01231935])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b26ab798",
   "metadata": {},
   "outputs": [],
   "source": [
    "Rfitting=5/np.exp(result[0])\n",
    "Cfitting=(-1/R/result[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4ab57ffe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9648322325645983"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Rfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "467b29fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9878305732142589"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6d19a7fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "ifitting=np.array(5/Rfitting*np.exp(-t/(Rfitting*Cfitting)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "00461ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2723e4ddca0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(t,np.log(ifitting),c='g')\n",
    "plt.scatter(t,np.log(i),c='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a4afc94",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calA2(X):\n",
    "    #X=np.array(X)\n",
    "    A=np.zeros((X.size,3))\n",
    "    A[:,0]=1\n",
    "    A[:,1]=X\n",
    "    A[:,2]=X**2\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dae6dfdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calb2(Y):\n",
    "    return Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b75dbd73",
   "metadata": {},
   "outputs": [],
   "source": [
    "t=np.linspace(-20,20,41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "a31a6ae5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-20., -19., -18., -17., -16., -15., -14., -13., -12., -11., -10.,\n",
       "        -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,   1.,\n",
       "         2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,\n",
       "        13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "429885f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=1\n",
    "b=2\n",
    "c=-3\n",
    "y=a+b*t+c*t**2+np.random.rand(t.size)-0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "7e90117c",
   "metadata": {},
   "outputs": [],
   "source": [
    "B=calb(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dc052b3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "A=calA2(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "52dcc76b",
   "metadata": {},
   "outputs": [],
   "source": [
    "result2=fitting(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "da886b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.89822682,  1.99965876, -2.99915909])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c6af5802",
   "metadata": {},
   "outputs": [],
   "source": [
    "yf=A@result2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f63ac022",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x272402682e0>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(t,yf,'b-')\n",
    "plt.scatter(t,y,c = 'yellow', edgecolors = 'blue',marker = '*',s = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "82c10476",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calAmax(X,m):\n",
    "    A=np.zeros((X.size,m+1))\n",
    "    for i in range(m+1):\n",
    "        A[:,i]=X**i\n",
    "    return A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2aec5242",
   "metadata": {},
   "outputs": [],
   "source": [
    "A=calAmax(t,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e35c6172",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=2\n",
    "y=a+b*t+c*t**2+d*t**3+np.random.rand(t.size)-0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "237314b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "B=calb(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "faba49ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "result3=fitting(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "2f9fe749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.98503554,  2.00969742, -2.99974439,  1.99998166])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "fae516b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "yf=A@result3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dfe53f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2724042ffa0>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.plot(t,yf,'b-')\n",
    "plt.scatter(t,y,c = 'yellow', edgecolors = 'blue',marker = '*',s = 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c0cd132",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81f90a2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef47a64c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
